
Spectral clustering (SC) [1]}: This method consists of a speech activity detection component (SAD) followed by clustering of small subsegment embeddings. We used a similar SAD as that described in [2]}, consisting of a TDNN-Stats based classifier with Viterbi decoding for inference. The speech segments were divided into subsegments with a window size of 1.5s and a stride of 0.75s, and 128-dimensional embeddings were extracted using an x-vector extractor [3]} trained on VoxCeleb data [4]}. The subsegment embeddings were scored pair-wise using cosine scoring, and spectral clustering was used to obtain speaker clusters. For estimating the number of speakers, we used an auto-tuning criterion based on \(p\) -binarization and normalized maximum eigengap [1]}.

VB-HMM based x-vector clustering (VBx) [6]}: Similar to the SC model, we used a TDNN-based SAD followed by subsegment-level embedding extraction using the same x-vector extractor. For clustering, we used VBx, which consists of a Bayesian HMM model. VB inference is used to iteratively refine the soft probabilistic alignment of x-vectors to speakers and re-estimate the speaker specific x-vector distributions. This inference is able to determine the number of speakers in the recording. The PLDA model used for VBx was trained on Librispeech utterances, and the speaker posterior matrix was initialized from the output of an agglomerative hierarchical clustering (AHC) system.

Region proposal networks (RPN) [7]}: This method combines segmentation and embedding extraction steps into a single neural network, and jointly optimizes them using an objective function that consists of boundary prediction and speaker classification components. The region embeddings are then clustered (using K-means clustering) and a non-maximal suppression is applied. We trained the RPN on simulated meeting-style recordings with partial overlaps generated using utterances from the Librispeech [8]} training set.

